From the 1 of 8 linked papers with an AI index.
8 papers
Overcoming the Modality Gap in Context-Aided Forecasting
Vincent Zhihao Zheng, Ãtienne Marcotte, Arjun Ashok +4
The paper introduces a semi‑synthetic data augmentation technique to create high‑quality contextual information for time‑series forecasting, producing a 7 million‑sample dataset (C…
Beyond Naïve Prompting: Strategies for Improved Context-aided Forecasting with LLMs
Arjun Ashok, Andrew Robert Williams, Vincent Zhihao Zheng +5
Real-world forecasting requires models to integrate not only historical data but also relevant contextual information provided in textual form. While large language models (LLMs) s…
Dr-CiK: A Testbed for Foresight-Driven Agents
Yihong Tang, Andrew Robert Williams, Arjun Ashok +6
Time series forecasting in real-world settings often depends not only on historical observations, but also on external context that must be actively discovered from noisy, heteroge…
Drinking water contamination as a population-wide determinant of mortality in California
Kiley Kennedy, Vincent Zheng, Benjamin Q Huynh
Drinking water contamination, a known determinant of adverse health outcomes, remains widespread and inequitably distributed amidst aging infrastructure. Regulatory oversight is th…
Frequency-Constrained Learning for Long-Term Forecasting
Menglin Kong, Vincent Zhihao Zheng, Lijun Sun
Many real-world time series exhibit strong periodic structures arising from physical laws, human routines, or seasonal cycles. However, modern deep forecasting models often fail to…
Dynamic Modes as Time Representation for Spatiotemporal Forecasting
Menglin Kong, Vincent Zhihao Zheng, Xudong Wang +1
This paper introduces a data-driven time embedding method for modeling long-range seasonal dependencies in spatiotemporal forecasting tasks. The proposed approach employs Dynamic M…